Building agent prototypes is easy. Running them in production is hard.
Most agent failures aren't model problems—they're data problems.
Your agents break when given real users
Your evaluation suite passes, but agents still break in production. Synthetic benchmarks can't replicate the long-tail edge cases and contextual complexity of actual user behavior.
No human feedback where it matters
Your agents handle edge cases the same way they handle routine tasks. There's no system to flag uncertain decisions for human review and turn that feedback into improvements.
Your agents don't learn from mistakes
There's no closed loop between production failures, human feedback, and agent improvements—every incident is a one-off fix instead of compounding knowledge that prevents future regressions.
How Zapire Works
The Engine for Reliability
01
Monitor
Production Traces
02
Filter
Intelligent Sampling
03
Label
Human-in-the-Loop
04
Optimize
Prompt & Memory
05
Deploy
Back to Production
01
Monitor Production
Track agent behavior in real-time
Capture execution traces from production traffic
Track agent decisions and outcomes
Identify patterns across user interactions
Build visibility into live agent behavior
02
Filter & Sample
Surface traces that need human review
Apply intelligent sampling to production traces
Identify uncertain or ambiguous cases
Prioritize edge cases and failures
Route the right traces to humans efficiently
03
Label with Humans
Get expert feedback where it matters
Let humans label trajectories, scores, and decisions
Provide reasons for failures and correct responses
Capture domain expertise at critical decision points
Create a curated dataset that reflects reality
04
Optimize with Data
Turn human feedback into improvements
Build training datasets from labeled traces
Optimize prompts
Turn coaching into memory so the agent handles similar cases better next time
Test different models systematically
Refine workflows based on real failures
05
Deploy Changes
Ship validated improvements with confidence
Run evals against your labeled dataset
Compare performance across versions
Validate that changes actually improve agents
Deploy knowing exactly what improved
Built for Production Teams
Who Uses Zapire?
AI Consultants
shipping agents for clients
Integrates With Your Stack
Works seamlessly with your existing tools
ObservabilityDatadog, Langsmith, Langfuse
WorkflowsLangGraph, PydanticAI, CrewAI
Your InfrastructureDrop-in integration, no migration required
What Zapire Isn't
We're focused on making your agents better, not replacing your stack.
Not a full orchestration framework
Not a labeling workforce
Not a comprehensive observability platform
Not foundation model training
We integrate with what you have and focus on closing the improvement loop.
What You Get
Agents follow your playbook, not generic defaults
Important decisions are raised to you automatically
Each review becomes memory, so repeated mistakes decline over time
More accurate agents in production
Faster identification of production issues
Rapid validation that fixes actually work
A growing failure-mode library that prevents regressions
Intelligent routing logic for human review
Measurable improvement in reliability over time
Design partner spots now open
Start Improving Your Agents Today
Build continual learning into production by turning failures into measurable model and workflow improvements with your existing stack.
No migration requiredWorks with your current toolingFast onboarding